Perceptions of response‐to‐intervention practices: results from a cross‐sectional survey of school‐board directors and support service professionals
Bibliographic record
Abstract
The response‐to‐intervention (RtI) model is widely used to support students with special educational needs and their teachers. However, little is known about how educational and health care professionals support the implementation of this model in specific contexts. The purpose of this study was to examine the perceptions of educational and health care professionals ( n = 101) regarding their involvement in RtI practices in Quebec, Canada. A cross‐sectional online survey was sent to school‐board directors responsible for overviewing support services ( n = 35), and to professionals implementing those services ( n = 66). Descriptive and content analyses were performed and perspectives of the two groups were compared. Regarding perspectives of RtI implementation, discrepancies were revealed between directors' perceptions of the involvement of their professionals versus the professionals' reported implementation of the RtI model in practice. Directors reported greater knowledge about RtI (84%) than professionals (72%). Professionals consistently reported lower involvement in supporting RtI implementation than did directors. Coaching was the most frequent activity reported and was mentioned by both directors and professionals as a service provided at all RtI tiers. Results reveal inconsistencies about tiered practices among administrators and school health care professionals, with individuals defining tiers differently and varying in practices implemented at specific tiers. Participants also provided useful suggestions to further implement RtI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".